Overview

Dataset statistics

Number of variables12
Number of observations124462
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 MiB
Average record size in memory104.0 B

Variable types

Categorical1
DateTime1
Text1
Numeric9

Alerts

metric7 is highly overall correlated with metric8High correlation
metric8 is highly overall correlated with metric7High correlation
failure is highly imbalanced (99.0%)Imbalance
metric2 is highly skewed (γ1 = 23.89959781)Skewed
metric3 is highly skewed (γ1 = 82.81730486)Skewed
metric4 is highly skewed (γ1 = 41.49760767)Skewed
metric7 is highly skewed (γ1 = 73.63460697)Skewed
metric8 is highly skewed (γ1 = 73.63460697)Skewed
metric9 is highly skewed (γ1 = 49.8928614)Skewed
metric2 has 118082 (94.9%) zerosZeros
metric3 has 115331 (92.7%) zerosZeros
metric4 has 115130 (92.5%) zerosZeros
metric7 has 123007 (98.8%) zerosZeros
metric8 has 123007 (98.8%) zerosZeros
metric9 has 97332 (78.2%) zerosZeros

Reproduction

Analysis started2023-06-22 07:54:13.668409
Analysis finished2023-06-22 07:54:22.188391
Duration8.52 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

failure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
124356 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Length

2023-06-22T15:54:22.240207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T15:54:22.328212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

date
Date

Distinct303
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2015-01-01 00:00:00
Maximum2015-10-31 00:00:00
2023-06-22T15:54:22.405618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:22.508072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

device
Text

Distinct1169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:22.677897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters995696
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS1F01085
2nd rowS1F0166B
3rd rowS1F01E6Y
4th rowS1F01JE0
5th rowS1F01R2B
ValueCountFrequency (%)
z1f0qlc1 303
 
0.2%
w1f0fzpa 303
 
0.2%
s1f0kycr 303
 
0.2%
z1f0qk05 303
 
0.2%
z1f0q8rt 303
 
0.2%
z1f0ql3n 303
 
0.2%
w1f0sjj2 303
 
0.2%
z1f0kjds 303
 
0.2%
z1f0ge1m 303
 
0.2%
z1f0gb8a 303
 
0.2%
Other values (1159) 121432
97.6%
2023-06-22T15:54:22.932583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 191959
19.3%
F 138423
13.9%
0 91178
 
9.2%
S 76236
 
7.7%
W 54962
 
5.5%
Z 39870
 
4.0%
L 25055
 
2.5%
3 23980
 
2.4%
K 18752
 
1.9%
B 17804
 
1.8%
Other values (24) 317477
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 593520
59.6%
Decimal Number 402175
40.4%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 138423
23.3%
S 76236
12.8%
W 54962
 
9.3%
Z 39870
 
6.7%
L 25055
 
4.2%
K 18752
 
3.2%
B 17804
 
3.0%
R 17712
 
3.0%
J 17504
 
2.9%
G 17261
 
2.9%
Other values (13) 169941
28.6%
Decimal Number
ValueCountFrequency (%)
1 191959
47.7%
0 91178
22.7%
3 23980
 
6.0%
6 15877
 
3.9%
5 15299
 
3.8%
2 14010
 
3.5%
4 13676
 
3.4%
7 12221
 
3.0%
9 12038
 
3.0%
8 11937
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
ÿ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 593521
59.6%
Common 402175
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 138423
23.3%
S 76236
12.8%
W 54962
 
9.3%
Z 39870
 
6.7%
L 25055
 
4.2%
K 18752
 
3.2%
B 17804
 
3.0%
R 17712
 
3.0%
J 17504
 
2.9%
G 17261
 
2.9%
Other values (14) 169942
28.6%
Common
ValueCountFrequency (%)
1 191959
47.7%
0 91178
22.7%
3 23980
 
6.0%
6 15877
 
3.9%
5 15299
 
3.8%
2 14010
 
3.5%
4 13676
 
3.4%
7 12221
 
3.0%
9 12038
 
3.0%
8 11937
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 995695
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 191959
19.3%
F 138423
13.9%
0 91178
 
9.2%
S 76236
 
7.7%
W 54962
 
5.5%
Z 39870
 
4.0%
L 25055
 
2.5%
3 23980
 
2.4%
K 18752
 
1.9%
B 17804
 
1.8%
Other values (23) 317476
31.9%
None
ValueCountFrequency (%)
ÿ 1
100.0%

metric1
Real number (ℝ)

Distinct123846
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2238518 × 108
Minimum0
Maximum2.4414048 × 108
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:23.049295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12089879
Q161271448
median1.2279194 × 108
Q31.8330837 × 108
95-th percentile2.3188178 × 108
Maximum2.4414048 × 108
Range2.4414048 × 108
Interquartile range (IQR)1.2203692 × 108

Descriptive statistics

Standard deviation70459869
Coefficient of variation (CV)0.57572222
Kurtosis-1.1992931
Mean1.2238518 × 108
Median Absolute Deviation (MAD)61031656
Skewness-0.011091131
Sum1.5232305 × 1013
Variance4.9645931 × 1015
MonotonicityNot monotonic
2023-06-22T15:54:23.155376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57192360 26
 
< 0.1%
165048912 26
 
< 0.1%
89196552 26
 
< 0.1%
169490248 23
 
< 0.1%
169467344 15
 
< 0.1%
165040624 15
 
< 0.1%
89162648 15
 
< 0.1%
57180136 15
 
< 0.1%
12194976 15
 
< 0.1%
165045144 13
 
< 0.1%
Other values (123836) 124273
99.8%
ValueCountFrequency (%)
0 11
< 0.1%
2048 1
 
< 0.1%
2056 2
 
< 0.1%
2168 1
 
< 0.1%
3784 1
 
< 0.1%
4224 1
 
< 0.1%
4480 1
 
< 0.1%
4560 1
 
< 0.1%
8280 1
 
< 0.1%
8616 1
 
< 0.1%
ValueCountFrequency (%)
244140480 1
< 0.1%
244138600 1
< 0.1%
244136552 1
< 0.1%
244135688 1
< 0.1%
244133240 1
< 0.1%
244132936 1
< 0.1%
244132752 1
< 0.1%
244131712 1
< 0.1%
244129416 1
< 0.1%
244127840 1
< 0.1%

metric2
Real number (ℝ)

SKEWED  ZEROS 

Distinct560
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.97226
Minimum0
Maximum64968
Zeros118082
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:23.256915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum64968
Range64968
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2172.2043
Coefficient of variation (CV)13.664047
Kurtosis629.39847
Mean158.97226
Median Absolute Deviation (MAD)0
Skewness23.899598
Sum19786005
Variance4718471.7
MonotonicityNot monotonic
2023-06-22T15:54:23.353514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118082
94.9%
2344 281
 
0.2%
8 260
 
0.2%
24 254
 
0.2%
40 201
 
0.2%
4960 175
 
0.1%
424 169
 
0.1%
16 166
 
0.1%
88 152
 
0.1%
552 140
 
0.1%
Other values (550) 4582
 
3.7%
ValueCountFrequency (%)
0 118082
94.9%
8 260
 
0.2%
16 166
 
0.1%
24 254
 
0.2%
32 132
 
0.1%
40 201
 
0.2%
48 90
 
0.1%
55 1
 
< 0.1%
56 103
 
0.1%
64 26
 
< 0.1%
ValueCountFrequency (%)
64968 1
 
< 0.1%
64792 6
 
< 0.1%
64784 11
< 0.1%
64776 8
< 0.1%
64736 13
< 0.1%
64728 13
< 0.1%
64584 17
< 0.1%
64472 1
 
< 0.1%
64464 1
 
< 0.1%
62296 1
 
< 0.1%

metric3
Real number (ℝ)

SKEWED  ZEROS 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9255837
Minimum0
Maximum24929
Zeros115331
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:23.452891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24929
Range24929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.67616
Coefficient of variation (CV)18.706826
Kurtosis10492.25
Mean9.9255837
Median Absolute Deviation (MAD)0
Skewness82.817305
Sum1235358
Variance34475.638
MonotonicityNot monotonic
2023-06-22T15:54:23.648428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 115331
92.7%
1 3273
 
2.6%
2 749
 
0.6%
7 298
 
0.2%
34 293
 
0.2%
5 278
 
0.2%
21 269
 
0.2%
4 267
 
0.2%
9 262
 
0.2%
8 251
 
0.2%
Other values (38) 3191
 
2.6%
ValueCountFrequency (%)
0 115331
92.7%
1 3273
 
2.6%
2 749
 
0.6%
3 113
 
0.1%
4 267
 
0.2%
5 278
 
0.2%
7 298
 
0.2%
8 251
 
0.2%
9 262
 
0.2%
10 241
 
0.2%
ValueCountFrequency (%)
24929 4
 
< 0.1%
2693 179
0.1%
2112 5
 
< 0.1%
1331 240
0.2%
1326 5
 
< 0.1%
1162 1
 
< 0.1%
406 84
 
0.1%
382 5
 
< 0.1%
378 1
 
< 0.1%
377 6
 
< 0.1%

metric4
Real number (ℝ)

SKEWED  ZEROS 

Distinct115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7412142
Minimum0
Maximum1666
Zeros115130
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:23.750785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum1666
Range1666
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.911388
Coefficient of variation (CV)13.158282
Kurtosis2467.3544
Mean1.7412142
Median Absolute Deviation (MAD)0
Skewness41.497608
Sum216715
Variance524.9317
MonotonicityNot monotonic
2023-06-22T15:54:23.845617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115130
92.5%
6 3680
 
3.0%
1 889
 
0.7%
2 710
 
0.6%
3 466
 
0.4%
12 453
 
0.4%
4 358
 
0.3%
10 294
 
0.2%
112 245
 
0.2%
5 231
 
0.2%
Other values (105) 2006
 
1.6%
ValueCountFrequency (%)
0 115130
92.5%
1 889
 
0.7%
2 710
 
0.6%
3 466
 
0.4%
4 358
 
0.3%
5 231
 
0.2%
6 3680
 
3.0%
7 174
 
0.1%
8 170
 
0.1%
9 45
 
< 0.1%
ValueCountFrequency (%)
1666 9
< 0.1%
1074 6
 
< 0.1%
1033 3
 
< 0.1%
841 1
 
< 0.1%
763 1
 
< 0.1%
533 1
 
< 0.1%
529 4
 
< 0.1%
521 6
 
< 0.1%
487 18
< 0.1%
486 15
< 0.1%

metric5
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.223474
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:23.948423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile58
Maximum98
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.944958
Coefficient of variation (CV)1.1210312
Kurtosis12.147989
Mean14.223474
Median Absolute Deviation (MAD)2
Skewness3.4831636
Sum1770282
Variance254.24168
MonotonicityNot monotonic
2023-06-22T15:54:24.047923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 22142
17.8%
9 13595
10.9%
11 12784
10.3%
10 11475
9.2%
7 11271
9.1%
12 9835
7.9%
6 8542
 
6.9%
13 6005
 
4.8%
14 3517
 
2.8%
5 3428
 
2.8%
Other values (50) 21868
17.6%
ValueCountFrequency (%)
1 173
 
0.1%
2 203
 
0.2%
3 815
 
0.7%
4 933
 
0.7%
5 3428
 
2.8%
6 8542
 
6.9%
7 11271
9.1%
8 22142
17.8%
9 13595
10.9%
10 11475
9.2%
ValueCountFrequency (%)
98 224
 
0.2%
95 672
0.5%
94 224
 
0.2%
92 448
0.4%
91 215
 
0.2%
90 357
0.3%
89 224
 
0.2%
78 224
 
0.2%
70 224
 
0.2%
68 448
0.4%

metric6
Real number (ℝ)

Distinct44809
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260148.77
Minimum8
Maximum689161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:24.148609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q1221447.25
median249794
Q3310234.25
95-th percentile443124.6
Maximum689161
Range689153
Interquartile range (IQR)88787

Descriptive statistics

Standard deviation99150.928
Coefficient of variation (CV)0.38113165
Kurtosis1.9082846
Mean260148.77
Median Absolute Deviation (MAD)35370
Skewness-0.37496442
Sum3.2378636 × 1010
Variance9.8309065 × 109
MonotonicityNot monotonic
2023-06-22T15:54:24.246329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 777
 
0.6%
44 708
 
0.6%
27 636
 
0.5%
26 520
 
0.4%
29 441
 
0.4%
36 337
 
0.3%
35 290
 
0.2%
52 282
 
0.2%
45 246
 
0.2%
28 216
 
0.2%
Other values (44799) 120009
96.4%
ValueCountFrequency (%)
8 19
 
< 0.1%
9 172
0.1%
12 51
 
< 0.1%
18 36
 
< 0.1%
19 30
 
< 0.1%
20 6
 
< 0.1%
21 58
 
< 0.1%
23 71
 
0.1%
24 123
0.1%
25 184
0.1%
ValueCountFrequency (%)
689161 1
< 0.1%
689062 1
< 0.1%
689035 1
< 0.1%
688964 1
< 0.1%
688952 2
< 0.1%
687901 1
< 0.1%
687802 1
< 0.1%
687775 1
< 0.1%
687706 1
< 0.1%
687694 2
< 0.1%

metric7
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29144639
Minimum0
Maximum832
Zeros123007
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:24.340173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4314916
Coefficient of variation (CV)25.498658
Kurtosis6897.9193
Mean0.29144639
Median Absolute Deviation (MAD)0
Skewness73.634607
Sum36274
Variance55.227068
MonotonicityNot monotonic
2023-06-22T15:54:24.421221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123007
98.8%
8 792
 
0.6%
16 397
 
0.3%
24 65
 
0.1%
48 36
 
< 0.1%
32 34
 
< 0.1%
128 23
 
< 0.1%
176 20
 
< 0.1%
40 20
 
< 0.1%
6 13
 
< 0.1%
Other values (18) 55
 
< 0.1%
ValueCountFrequency (%)
0 123007
98.8%
6 13
 
< 0.1%
8 792
 
0.6%
16 397
 
0.3%
22 2
 
< 0.1%
24 65
 
0.1%
32 34
 
< 0.1%
40 20
 
< 0.1%
48 36
 
< 0.1%
56 6
 
< 0.1%
ValueCountFrequency (%)
832 2
 
< 0.1%
744 1
 
< 0.1%
736 4
 
< 0.1%
496 1
 
< 0.1%
424 1
 
< 0.1%
312 5
 
< 0.1%
272 2
 
< 0.1%
240 1
 
< 0.1%
216 1
 
< 0.1%
176 20
< 0.1%

metric8
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29144639
Minimum0
Maximum832
Zeros123007
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:24.504852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4314916
Coefficient of variation (CV)25.498658
Kurtosis6897.9193
Mean0.29144639
Median Absolute Deviation (MAD)0
Skewness73.634607
Sum36274
Variance55.227068
MonotonicityNot monotonic
2023-06-22T15:54:24.584725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 123007
98.8%
8 792
 
0.6%
16 397
 
0.3%
24 65
 
0.1%
48 36
 
< 0.1%
32 34
 
< 0.1%
128 23
 
< 0.1%
176 20
 
< 0.1%
40 20
 
< 0.1%
6 13
 
< 0.1%
Other values (18) 55
 
< 0.1%
ValueCountFrequency (%)
0 123007
98.8%
6 13
 
< 0.1%
8 792
 
0.6%
16 397
 
0.3%
22 2
 
< 0.1%
24 65
 
0.1%
32 34
 
< 0.1%
40 20
 
< 0.1%
48 36
 
< 0.1%
56 6
 
< 0.1%
ValueCountFrequency (%)
832 2
 
< 0.1%
744 1
 
< 0.1%
736 4
 
< 0.1%
496 1
 
< 0.1%
424 1
 
< 0.1%
312 5
 
< 0.1%
272 2
 
< 0.1%
240 1
 
< 0.1%
216 1
 
< 0.1%
176 20
< 0.1%

metric9
Real number (ℝ)

SKEWED  ZEROS 

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.454524
Minimum0
Maximum18701
Zeros97332
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T15:54:24.677185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum18701
Range18701
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.45017
Coefficient of variation (CV)15.371938
Kurtosis4049.1503
Mean12.454524
Median Absolute Deviation (MAD)0
Skewness49.892861
Sum1550115
Variance36653.169
MonotonicityNot monotonic
2023-06-22T15:54:24.773577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97332
78.2%
1 9434
 
7.6%
2 3721
 
3.0%
3 2327
 
1.9%
4 1395
 
1.1%
6 797
 
0.6%
7 774
 
0.6%
5 735
 
0.6%
8 733
 
0.6%
10 640
 
0.5%
Other values (56) 6574
 
5.3%
ValueCountFrequency (%)
0 97332
78.2%
1 9434
 
7.6%
2 3721
 
3.0%
3 2327
 
1.9%
4 1395
 
1.1%
5 735
 
0.6%
6 797
 
0.6%
7 774
 
0.6%
8 733
 
0.6%
9 335
 
0.3%
ValueCountFrequency (%)
18701 5
 
< 0.1%
10137 4
 
< 0.1%
7226 5
 
< 0.1%
2794 6
 
< 0.1%
2637 84
0.1%
2522 179
0.1%
2270 5
 
< 0.1%
2269 1
 
< 0.1%
1864 5
 
< 0.1%
1165 118
0.1%

Interactions

2023-06-22T15:54:20.948907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:14.814143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.601631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.367280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.116369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.876914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.620970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.497989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.223659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.036596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:14.911287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.686575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.452222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.201763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.960545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.707868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.580085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.305252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.123998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.000967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.775625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.536795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.288981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.044978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.797532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.661099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.387031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.208885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.086051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.858424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.618386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.371043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.126739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.884515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.740775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.466127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.294715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.172968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.944552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.702150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.456247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.209539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.974114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.823656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.548302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.380235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.258387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.026791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.783462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.537198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.289899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.058905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.902817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.627141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.470465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.349107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.116934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.871802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.627080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.377115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.149614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.988484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.713966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.551302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.429832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.197476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.951259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.706672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.456671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.232550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.063498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.787664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:21.632085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:15.511986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:16.278162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.028512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:17.788350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:18.533917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:19.408531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.139035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T15:54:20.863314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-22T15:54:24.860792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
metric1metric2metric3metric4metric5metric6metric7metric8metric9failure
metric11.000-0.0010.0020.002-0.005-0.003-0.002-0.002-0.0030.009
metric2-0.0011.000-0.0190.225-0.027-0.0780.1090.109-0.0290.096
metric30.002-0.0191.0000.1210.1070.070-0.010-0.0100.3900.000
metric40.0020.2250.1211.000-0.0210.0120.1630.1630.0490.112
metric5-0.005-0.0270.107-0.0211.0000.083-0.020-0.0200.0340.007
metric6-0.003-0.0780.0700.0120.0831.000-0.016-0.0160.0900.012
metric7-0.0020.109-0.0100.163-0.020-0.0161.0001.000-0.0180.114
metric8-0.0020.109-0.0100.163-0.020-0.0161.0001.000-0.0180.114
metric9-0.003-0.0290.3900.0490.0340.090-0.018-0.0181.0000.000
failure0.0090.0960.0000.1120.0070.0120.1140.1140.0001.000

Missing values

2023-06-22T15:54:21.778318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-22T15:54:22.005108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

failuredatedevicemetric1metric2metric3metric4metric5metric6metric7metric8metric9
001/1/2015S1F01085215630672550526407438007
101/1/2015S1F0166B613706800306403174000
201/1/2015S1F01E6Y17329596800012237394000
301/1/2015S1F01JE0796940240006410186000
401/1/2015S1F01R2B13597048000015313173003
501/1/2015S1F01TD56883748800416413535001
601/1/2015S1F01XDJ2277216320008402525000
701/1/2015S1F023H21415036000011949446216163
801/1/2015S1F02A0J821784001014311869000
901/1/2015S1F02DZ211644009603789940790500170
failuredatedevicemetric1metric2metric3metric4metric5metric6metric7metric8metric9
124453010/31/2015W1F0SJJ214454263200012355333000
124454010/31/2015Z1F0GB8A1703088720009355070000
124455010/31/2015Z1F0GE1M23577779200010349229000
124456010/31/2015Z1F0KJDS16193500800011355984000
124457010/31/2015Z1F0KKN41716091680009350844000
124458010/31/2015Z1F0MA1S18431039200010353052000
124459010/31/2015Z1F0Q8RT184353576961074113321660013
124460010/31/2015Z1F0QK053091288048320011349802000
124461010/31/2015Z1F0QL3N6201810400012356937000
124462010/31/2015Z1F0QLC13211736800010350840000